As technology continues to advance, more methods of tracking nearly every aspect of a business are developed. Customers' interactions on websites can be tracked. Users' posts to social networks can be viewed. Detailed sales, logistics, and marketing effectiveness data is collected and analyzed. Data sets will continue to grow in size because they are increasingly being gathered by ubiquitous information-sensing mobile devices, remote sensing technologies, software logs, cameras, microphones, radio-frequency identification technology, and a plethora of other sensor networks and tracking systems which exist all around us. In sum, about 90% of the data in the history of the world today was created within the past two years. With the advances in the collection of data hitting the market with such force, businesses are facing the ever-daunting challenge of figuring out how to most effectively use these massive amounts of information to achieve their business goals.
This abundance of data is typically referred to as “big data.” In Big Data: The Management Revolution, HARVARD BUSINESS REVIEW, October 2012, authors Andrew McAfee and Erik Brynjolfsson consider the impact of big data on companies' performance. For example, it is noted that, as of 2012, 2.5 exabytes of data created each day, and that number is doubling roughly every forty (40) months. In fact, more data cross the internet every second presently than were stored in the entire internet just twenty (20) years ago! Big data takes the form of messages, updates, and images posted to social networks; readings from sensors, GPS signals from cell phones, etc. Given the onset of the big data revolution, it is critical that companies adopt techniques and tools designed to leverage the insights that big data can provide.
Businesses, themselves, typically do not have the capability or capacity to handle this large amount of data. Many companies therefore scale-down available datasets to a more manageable size or use sample sizes and create inelegant methods of managing this remaining information. Many marketing, sales and business managers will use a conventional spreadsheet to manage and visualize data; a tedious process which ignores the power available in full datasets. In order to keep up with the amount of information available to companies, and in order to keep ahead of competition, many of these businesses turn to external vendors in order to manage this unwieldy amount of business data.
Application service providers have grown to play an ever-increasing role in the space of business intelligence. Many Internet companies provide their own reporting tools and advertising platforms or other 3rd party technology providers to users of their services in order to keep those users dedicated customers. Applications such as Google Analytics. Epsilon, Doubleclick, LinkShare, and the like allow users to monitor individuals accessing their websites and online media on a highly detailed level. Unfortunately, each of these online services cultivates a “walled garden” of information which creates segmented pockets of data across a set of services. Accordingly, business users traditionally were required to download data from each of these services and aggregate this disparate data on their own. Such a daunting task often led to undesirable or inefficient results.
Over the past few years, information technology companies have rushed to create a market for the analysis of these segmented pools of data. Consulting groups branched out of traditional IT services powerhouses to provide assistance to individual businesses in the collection, storage, and management of their business data. The focus of these consultancies was, and still is, to take off-the-shelf hardware and software in order to create a custom, proprietary solution for each individual business. The prevailing postulate amongst these types of consultants, and the information technology departments working with those consultants, is that business intelligence data is the property of the underlying business and should therefore be both safeguarded and managed on company-owned systems. This conventional thinking creates the unfortunate situation whereby any business, government or other enterprise cannot automate data management to take advantage of economies of scale or insights generated by trusted parties partners other divisions or non-competitive industries around non-proprietary data or useful third party data.
As the market evolved, managed service providers developed which sought to eliminate the need for additional staff and the expensive hardware ownership costs from the equation. These managed service providers would own the requisite hardware, software licenses, and consulting know-how in-house and provide the service of utilization and configuration of a custom business intelligence system for its customers. While this new model reduces complexity to the purchaser of business analytics hardware and software, it merely pushed the previous in-house ownership model to an off-site leased engagement. The business customer's data was still segmented and segregated and there was still a need for custom configuration and management of the business intelligence system by either a trained professional working for the business customer or a consultant provided by the managed service provider. Further, businesses were still constrained to the limits of their custom systems and were presented prohibitively high costs to change or adjust those systems to meet new challenges and business needs as the market evolved. Time is also a consideration as it takes much longer to get answers from a managed service provider.
There exists a need for an improved method of business intelligence, knowledge management and decision support (such terms herein referred to both collectively and interchangeably) wherein the overall cost of ownership and burden of management is further reduced. There further exists a need for a method of better analyzing and collaborating in the business intelligence, knowledge management and decision support field in order to empower decision makers with information to yield creative and desired results.
Other problems and drawbacks also exist. For example, current industry data models cannot support multiple companies, from different verticals (such as banking, retail, pharmaceutical and the like) on a single platform running simultaneously across the same systems and software. These systems were enterprise based—namely designed to support a single organization, typically in one vertical with access only open to a limited number of business users employed by that organization and from within systems housed in the organization's technical infrastructure customization was needed. Thus, an important need remains to permit multiple organizations and their employees, consultants and advisers to access and perform business intelligence on a common pool of data. The inventors created the first multi-tenant, multi-party and multi-enterprise platform (such terms herein referred to both collectively and interchangeably) for business intelligence, covering all of the components of a complete BI solution in a seamless efficient paradigm.